In 2023, New Mexico had the highest burglary rate in the United States. That year, they had 517.9 occurrences per 100,000 residents. Washington followed with 481 incidents per 100,000 residents. What is burglary? Burglary in the United States is considered a felony or misdemeanor. It includes trespassing and theft, and going inside a building or car with the intent to commit any crime. Even if the crime is not necessarily theft, it is still illegal. Some states consider burglary committed during the day as housebreaking, not burglary. The Bureau of Justice Statistics defines it as unlawful or forcible entry into a building. There are four types of burglary in total: completed burglary, forcible entry, unlawful entry, and attempted forcible entry. Burglary in the United States Burglary affects all 50 states in the United States, as burglary was the third most common type of property crime in the United States in 2023. California had the highest number of reported burglaries in that same year, whereas New Hampshire had the lowest number. However, the overall reported burglary rate in the United States has decreased significantly since 1990.

In 2023, the nationwide burglary rate in the United States was 250.7 cases per 100,000 of the population. This is a slight decrease from the previous year, when the burglary rate stood at 272.7 cases per 100,000 of the population.

In 2022, Costa Rica had the highest burglary rate worldwide, with ***** occurrences per 100,000 inhabitants. Other countries with the highest burglary rate were Sweden, Luxembourg and Dominica.

When incidents happened, information about offenders, the victim’s perception of the incident, and what items were stolen. Annual data from the Crime Survey for England and Wales (CSEW).

The average for 2017 based on 79 countries was 105 robberies per 100,000 people. The highest value was in Costa Rica: 1587 robberies per 100,000 people and the lowest value was in Oman: 1 robberies per 100,000 people. The indicator is available from 2003 to 2017. Below is a chart for all countries where data are available.

In 2023, an estimated 839,563 reported burglary cases occurred across the United States, a slight decrease from the previous year. The number of reported burglaries has been decreasing since 1990, when there were 3.07 million reported burglaries nationwide.

The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ The following datasets contain the crime rate for cities in the United States. The four datasets are separated based on population ranges.

FILE DESCRIPTION

File names: - 'crime_40 _60.csv': dataset for population ranging from 40,000 to 60,000. - 'crime_60 _100.csv': dataset for population ranging from 60,000 to 100,000. - 'crime_100 _250.csv': dataset for population ranging from 100,000 to 250,000. - 'crime_250 _plus.csv': dataset for population greater than 250,000.

COLUMN DESCRIPTION

For file: crime_40 _60.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_60 _100.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_100 _250.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'violent_crime': violent crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft

crime_250 _plus.csv: - 'states': name of the state - 'cities': name of the city - 'population': population of the city - 'total_crime': total crime - 'murder': murder and nonnegligent manslaughter - 'rape': forcible rape - 'robbery': robbery - 'agrv_ assault': agrv_ assault - 'total_violent _crime': total violent crime - 'prop_crime': property crime - 'burglary': burglary - 'larceny': larceny theft - 'vehicle_theft': motor vehicle theft - 'tot_prop _crime': total property crime - 'arson': arson

Photo by David von Diemar on Unsplash

These data examine the effects on total crime rates of changes in the demographic composition of the population and changes in criminality of specific age and race groups. The collection contains estimates from national data of annual age-by-race specific arrest rates and crime rates for murder, robbery, and burglary over the 21-year period 1965-1985. The data address the following questions: (1) Are the crime rates reported by the Uniform Crime Reports (UCR) data series valid indicators of national crime trends? (2) How much of the change between 1965 and 1985 in total crime rates for murder, robbery, and burglary is attributable to changes in the age and race composition of the population, and how much is accounted for by changes in crime rates within age-by-race specific subgroups? (3) What are the effects of age and race on subgroup crime rates for murder, robbery, and burglary? (4) What is the effect of time period on subgroup crime rates for murder, robbery, and burglary? (5) What is the effect of birth cohort, particularly the effect of the very large (baby-boom) cohorts following World War II, on subgroup crime rates for murder, robbery, and burglary? (6) What is the effect of interactions among age, race, time period, and cohort on subgroup crime rates for murder, robbery, and burglary? (7) How do patterns of age-by-race specific crime rates for murder, robbery, and burglary compare for different demographic subgroups? The variables in this study fall into four categories. The first category includes variables that define the race-age cohort of the unit of observation. The values of these variables are directly available from UCR and include year of observation (from 1965-1985), age group, and race. The second category of variables were computed using UCR data pertaining to the first category of variables. These are period, birth cohort of age group in each year, and average cohort size for each single age within each single group. The third category includes variables that describe the annual age-by-race specific arrest rates for the different crime types. These variables were estimated for race, age, group, crime type, and year using data directly available from UCR and population estimates from Census publications. The fourth category includes variables similar to the third group. Data for estimating these variables were derived from available UCR data on the total number of offenses known to the police and total arrests in combination with the age-by-race specific arrest rates for the different crime types.

Incident-based crime statistics (actual incidents, rate per 100,000 population, percentage change in rate, unfounded incidents, percent unfounded, total cleared, cleared by charge, cleared otherwise, persons charged, adults charged, youth charged / not charged), by detailed violations (violent, property, traffic, drugs, other Federal Statutes), police services in Ontario, 1998 to 2024.

Incident-based crime statistics (actual incidents, rate per 100,000 population, percentage change in rate, unfounded incidents, percent unfounded, total cleared, cleared by charge, cleared otherwise, persons charged, adults charged, youth charged / not charged), by detailed violations (violent, property, traffic, drugs, other Federal Statutes), Canada, provinces, territories, Census Metropolitan Areas and Canadian Forces Military Police, 1998 to 2024.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The study integrated neighborhood-level data on robbery and burglary gathered from local police agencies across the United States, foreclosure data from RealtyTrac (a real estate information company), and a wide variety of social, economic, and demographic control variables from multiple sources. Using census tracts to approximate neighborhoods, the study regressed 2009 neighborhood robbery and burglary rates on foreclosure rates measured for 2007-2008 (a period during which foreclosure spiked dramatically in the nation), while accounting for 2007 robbery and burglary rates and other control variables that captured differences in social, economic, and demographic context across American neighborhoods and cities for this period. The analysis was based on more than 7,200 census tracts in over 60 large cities spread across 29 states. Core research questions were addressed with a series of multivariate multilevel and single-level regression models that accounted for the skewed nature of neighborhood crime patterns and the well-documented spatial dependence of crime. The study contains one data file with 8,198 cases and 99 variables.

Panel data on the crime rate of burglary and the clearance rate of the police at the regional level in Germany from 2013 to 2017. Data was retrieved from the annual crime report (PKS) of the German Federal Police (BKA).

In 2023, the federal state of California had the most reported burglaries in the United States, with a total of 135,369 reported cases. Texas, North Carolina, Washington, and Florida rounded out the top five states with the most burglaries in that year.

This is a dataset on reported swedish crime statistics from 1950 to 2023 taken from https://bra.se/bra-in-english/home/crime-and-statistics/crime-statistics.html

The dataset includes - Total number of crimes - Life and Death crimes - Murder, Manslaughter and lethal assault - Assault/Aggravated Assult - Sexual Offences - Rape, Aggravated Rape - Theft, Robbery, Stealing - Burglary not involving firearm - Burglary in flats, Private Houses - Vehicle theft - Theft out of a motor vehicle - Theft from shops, Department stores etc. - Robbery, Aggravated robbery - Fraud and other acts of dishonesty - Criminal Damage - Narcotics - Driving under the influence

Things to consider:

1 Statistic figures on reported completed murder and manslaughter are higher than the actual number of murder and manslaughter.

2 Year 1950-1984 also including "sexual violation".

3 Year 1965-1967 also including burglary involving fire arms, ammunition and explosives.

4 Including crimes against the Narcotics Regulation year 1950-1983. From 1 July 1983 crimes against the Narcotics Regulation is covered by the Narcotics Drugs (Penal) Act section 5a.

5 Since 1 July 1999 also including driving under the influence of illicit drugs.

6 Four cases of embezzlement reported in 1996 covered appx 24'500 offences.

7 Statistics on crimes against creditors (Penal code ch. 11) and tax crimes are incomplete year 1998 and 1999 due incomplete reporting to Brå.

8 Data for year 1999 and 2000 may have been affected by a system change in year 1999/2000.

9 Excluding data where crime is uncategorized. These data were removed from the statistics from year 2003.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study was a secondary analysis of data from the National Crime Victimization Survey (NCVS) and National Incidents Based Reporting System (NIBRS) for the period 1998-2007. The analysis calculates two separate measures of the incidents of violence that occurred during burglaries. The study addressed the following research questions: Is burglary a violent crime? Are different levels of violence associated with residential versus nonresidential burglaries? How frequently is a household member present during a residential burglary? How frequently does violence occur in the commission of a burglary? What forms does burglary-related violence take? Are there differences in rates of violence between attempted and completed burglaries? What constitutes the crime of burglary in current statutory law? How do the federal government and the various states define burglary (grades and elements)? Does statutory law comport with empirical observations of what the typical characteristics of acts of burglary are? The SPSS code distributed here alters an existing dataset drawn from pre-existing studies. In order to use this code users must first create the original data file drawn from National Crime Victimization Survey (NCVS) and National Incidents Based Reporting System (NIBRS) data from the period of 1998-2007. All data used for this study are publicly available through ICPSR. See the variable description section for a comprehensive list of, and direct links to, all datasets used to create this original dataset.

Crime data assembled by census block group for the MSA from the Applied Geographic Solutions' (AGS) 1999 and 2005 'CrimeRisk' databases distributed by the Tetrad Computer Applications Inc. CrimeRisk is the result of an extensive analysis of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, CrimeRisk provides an accurate view of the relative risk of specific crime types at the block group level. Data from 1990 - 1996,1999, and 2004-2005 were used to compute the attributes, please refer to the 'Supplemental Information' section of the metadata for more details. Attributes are available for two categories of crimes, personal crimes and property crimes, along with total and personal crime indices. Attributes for personal crimes include murder, rape, robbery, and assault. Attributes for property crimes include burglary, larceny, and mother vehicle theft. 12 block groups have no attribute information. CrimeRisk is a block group and higher level geographic database consisting of a series of standardized indexes for a range of serious crimes against both persons and property. It is derived from an extensive analysis of several years of crime reports from the vast majority of law enforcement jurisdictions nationwide. The crimes included in the database are the "Part I" crimes and include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. These categories are the primary reporting categories used by the FBI in its Uniform Crime Report (UCR), with the exception of Arson, for which data is very inconsistently reported at the jurisdictional level. Part II crimes are not reported in the detail databases and are generally available only for selected areas or at high levels of geography. In accordance with the reporting procedures using in the UCR reports, aggregate indexes have been prepared for personal and property crimes separately, as well as a total index. While this provides a useful measure of the relative "overall" crime rate in an area, it must be recognized that these are unweighted indexes, in that a murder is weighted no more heavily than a purse snatching in the computation. For this reason, caution is advised when using any of the aggregate index values. The block group boundaries used in the dataset come from TeleAtlas's (formerly GDT) Dynamap data, and are consistent with all other block group boundaries in the BES geodatabase. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

This dataset provides comprehensive data on violent and property crimes in Maryland counties from 1975 to the present. It includes various crime metrics such as murder, rape, robbery, aggravated assault, burglary, larceny theft, motor vehicle theft, and more.

Columns Information

JURISDICTION

• Description: County name in Maryland.
• Data Type: String

YEAR

• Description: Year of the data.
• Data Type: Integer

POPULATION

• Description: Population of the county.
• Data Type: Integer

MURDER

• Description: Number of murders.
• Data Type: Integer

RAPE

• Description: Number of rapes.
• Data Type: Integer

ROBBERY

• Description: Number of robberies.
• Data Type: Integer

AGG. ASSAULT

• Description: Number of aggravated assaults.
• Data Type: Integer

B & E

• Description: Number of burglaries (breaking and entering).
• Data Type: Integer

LARCENY THEFT

• Description: Number of larceny thefts.
• Data Type: Integer

M/V THEFT

• Description: Number of motor vehicle thefts.
• Data Type: Integer

GRAND TOTAL

• Description: Total number of crimes.
• Data Type: Integer

PERCENT CHANGE

• Description: Percent change in total crimes from the previous year.
• Data Type: Float

VIOLENT CRIME TOTAL

• Description: Total number of violent crimes.
• Data Type: Integer

VIOLENT CRIME PERCENT

• Description: Percent of violent crimes out of total crimes.
• Data Type: Float

VIOLENT CRIME PERCENT CHANGE

• Description: Percent change in violent crimes from the previous year.
• Data Type: Float

PROPERTY CRIME TOTALS

• Description: Total number of property crimes.
• Data Type: Integer

PROPERTY CRIME PERCENT

• Description: Percent of property crimes out of total crimes.
• Data Type: Float

PROPERTY CRIME PERCENT CHANGE

• Description: Percent change in property crimes from the previous year.
• Data Type: Float

OVERALL CRIME RATE PER 100,000 PEOPLE

• Description: Crime rate per 100,000 people.
• Data Type: Float

OVERALL PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in crime rate per 100,000 people from the previous year.
• Data Type: Float

VIOLENT CRIME RATE PER 100,000 PEOPLE

• Description: Violent crime rate per 100,000 people.
• Data Type: Float

VIOLENT CRIME RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in violent crime rate per 100,000 people from the previous year.
• Data Type: Float

PROPERTY CRIME RATE PER 100,000 PEOPLE

• Description: Property crime rate per 100,000 people.
• Data Type: Float

PROPERTY CRIME RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in property crime rate per 100,000 people from the previous year.
• Data Type: Float

MURDER PER 100,000 PEOPLE

• Description: Murder rate per 100,000 people.
• Data Type: Float

RAPE PER 100,000 PEOPLE

• Description: Rape rate per 100,000 people.
• Data Type: Float

ROBBERY PER 100,000 PEOPLE

• Description: Robbery rate per 100,000 people.
• Data Type: Float

AGG. ASSAULT PER 100,000 PEOPLE

• Description: Aggravated assault rate per 100,000 people.
• Data Type: Float

B & E PER 100,000 PEOPLE

• Description: Burglary (breaking and entering) rate per 100,000 people.
• Data Type: Float

LARCENY THEFT PER 100,000 PEOPLE

• Description: Larceny theft rate per 100,000 people.
• Data Type: Float

M/V THEFT PER 100,000 PEOPLE

• Description: Motor vehicle theft rate per 100,000 people.
• Data Type: Float

MURDER RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in murder rate per 100,000 people from the previous year.
• Data Type: Float

RAPE RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in rape rate per 100,000 people from the previous year.
• Data Type: Float

ROBBERY RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in robbery rate per 100,000 people from the previous year.
• Data Type: Float

AGG. ASSAULT RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in aggravated assault rate per 100,000 people from the previous year.
• Data Type: Float

B & E RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in burglary (breaking and entering) rate per 100,000 people from the previous year.
• Data Type: Float

LARCENY THEFT RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in larceny theft rate per 100,000 people from the previous year.
• Data Type: Float

M/V THEFT RATE PERCENT CHANGE PER 100,000 PEOPLE

• Description: Percent change in motor vehicle t...

In 2020, Hot Springs, Arkansas had the highest burglary rate in the United States, with 1,202.9 cases of burglary per 100,000 of its inhabitants. Lake Charles, Louisiana had the second highest burglary rate, at 1,065.7 cases per 100,000 inhabitants.